CompTIA DataAI Certification

Price
Net: €
VAT: €

Price
Price on Request

Duration
5 days

For companies and job seekers:
this course is 100% fundable!
 

Location

Course Language
English

Training Solutions
Online Live

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Any AI is only as good as the data behind it. The intersection of data quality, analysis, and intelligent technologies creates an area of expertise that provides crucial support to companies in automation and innovation.

Key topics:

  • Modern data ecosystems and analytical methods
  • Applications of artificial intelligence
  • Training, evaluation, and deployment of AI models
  • Data-driven optimization of business processes
  • Governance, risks, and ethical requirements

Prerequisites
Knowledge of digital applications, IT fundamentals, or data processes provides a helpful foundation.

Target Audience
This course is designed for professionals working at the intersection of technology, analytics, and digital transformation.

Data only yields real insights once it is understood. Skills in analytics and artificial intelligence help ensure that technologies are used sensibly and responsibly.

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Course Content
  • Statistical methods and data-driven analysis techniques.
  • Probability models and synthetic modeling.
  • Fundamentals of linear algebra and calculus.
  • Time series models and their applications.
  • Methods and Techniques of Exploratory Data Analysis (EDA).
  • Analysis and evaluation of typical data problems.
  • Techniques for data enrichment and expansion.
  • Iterative development and optimization of models.
  • Evaluation of tests and experiments for model selection.
  • Presentation of results and clear communication of findings.
  • Apply the fundamentals of machine learning.
  • Understand supervised learning methods and statistical models.
  • Apply tree-based machine learning methods.
  • Explain concepts of deep learning.
  • Understand unsupervised learning and key methods.
  • Understand the role of data science in various business areas.
  • Classify data types, data collection, and their potential applications.
  • Learn the basics of data collection and storage.
  • Apply methods for data preparation and cleaning.
  • Implement best practices in the data science lifecycle.
  • Understand the importance of DevOps and MLOps for data science projects.
  • Compare and evaluate different deployment environments.
  • Related topics in the field of AI, including NLP and computer vision.

Frequently Asked Questions

  • AI only works with high-quality data. This certification combines data analysis and artificial intelligence to better evaluate information and implement intelligent solutions.
  • In-demand skills in data, AI models, and analytics help you transition into modern roles at the intersection of IT, business intelligence, and artificial intelligence.
  • The focus is on modern analytical tools, AI applications, data platforms, and methods for processing, evaluating, and utilizing large amounts of data.
  • In addition to data literacy, the focus is on intelligent systems: recognizing patterns, optimizing processes, and effectively integrating AI technologies into data-driven decisions.
  • Evaluate data quality, understand AI results, support automation, and derive actionable insights from complex information.
  • Responsible AI requires transparent data. Issues such as data management, quality, and evaluation help us better assess AI results.
  • Ideal for the intersection of IT, analytics, and business—anywhere data is used to drive processes, strategies, and innovation.
  • Flexible knowledge instead of platform lock-in: The content can be applied to various technologies, systems, and digital projects.
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Do you have any further questions? Please contact us.